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In business and in science, you will often see sets of data that have been gathered using suboptimal techniques, leading to poor quality predictions and marring any insight that you might gain from analyzsing these data sets. In general, for most purposes, you want to gather quantitative data.
Quantitative data is a data type which revolves around collecting numerical data rather than qualitative data. Qualitative data is much more ephemeral, lacking the impact and being much less useful than quantitative data.
Qualitative data is often collected in fields where you cannot gain numerical answers, such as psychology and other social sciences. If you are having trouble devising a system that allows you to collect sufficient quality and quantity of qualitative data for your business or project, you could try out this Inferential Statistics in SPSS c ourse, which is based in the SPSS statistical analysis program written by IBM, but can be applied to any type of statistics work.
What Exactly are Quantitative Techniques? The term quantitative techniques covers a broad range of statistical gathering techniques that are all focused on getting numerical data for statistical analysis.
These statistics are often then used for research and analysis leading to business decisions. These quantitative results can come from a wide variety of sources. The best quality data that you can get, in terms of quantitative techniques, is the double blind test. A double blind test gives the most accurate results as any bias that might occur in the test subjects or the tester will not be represented in the result.
A double blind test makes it so both the tester and the test subjects in the experiment do not know the true reason for the experiment and are often told that the experiment is testing something else completely.
Whilst you could just ask the test subjects to be open minded and try not to use bias over the course of the experiment, it is often unconscious bias that is reflected in the data sets.
If you are testing a drug that makes people stronger, you give half of the test subjects a fake, sugar pill which is often known as a placebo, whilst giving the other half of the group the real drug.
If you were to give everyone the same drug, any effects you see in the patients may not be because of the drug as the human mind is very easily manipulated. The placebo group are used as a type of baseline for any experiment. In business, there are many other types of quantitative techniques you might apply to your data.
All quantitative techniques fall broadly under the umbrellas of mathematical, statistical, or programming based techniques and each has their own benefits and drawbacks. Most businesses will use multiple techniques simultaneously as this will give the company a more rounded picture of how to use the data correctly.
Quantitative techniques are much more accurate than Qualitative techniques, as they eliminate the bias associated with both qualitative tests and non blind tests. Differentiation A popular type of quantitative technique is differentiation. Differentiation is a mathematical process involving calculus and it is useful for seeing change over time within a given system.
Differentiation is generally used to figure out the changes in a system when a variable in the system changes, measuring how the end result changes by altering a variable.
This could be used in many ways: Differentiation also has an opposite, integration, which works in the opposite way. Integration is used to see the changes to a variable when the system changes. Both of these are valuable quantitative techniques to learn and are very difficult to get your head around.
The mathematics involved is very high level and people often struggle with it even after being taught how to do it.
This course in Integral Calculus will serve you well in remembering or even learning calculus for the first time. The course is fantastic and will help you develop your quantitative data analysis techniques and also will teach you in easy understandable steps how to use calculus for many different situations.
Regression Analysis Regression analysis is incredibly useful and a whole host of people use this technique every single day in their business life. Generally, economists are interested in the concept of regression analysis, which is based around finding a causal link or correlation between two independent variables in any given system.
A common example for regression analysis is that of measuring the salary of an employee and their level of education, to see if there is a correlation between the two factors. You could also use this in cooking and many other fields, as you can see. Regression analysis is useable in many fields and will save you time if you learn how to use it and integrate in to your business.
Regression analysis uses two sets of data, predictors and independent variables. These values can be anything, from total revenue to tax rate to advertisement budgets and so on.
Comparing the two is the basis of regression analysis. Simulation Simulation is a great way to get pseudo real world data on anything that can be simulated effectively in a controlled environment. If you can simulate a scenario effectively, you can then see how test subjects respond to stressors and often this information is very valuable.
This data allows the manufacturer to make tweaks in design and concept and can show data which may lead to the product being discontinued before production starts. This is obviously a good thing as recalling product lines is costly and should be avoided at all costs.Overview.
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Quantitative Techniques in Business, Management and Finance: A Case-Study Approach - CRC Press Book This book is especially relevant to undergraduates, postgraduates and researchers studying quantitative techniques as part of business, management and finance. Description: This course covers the fundamental aspects of analytical tools including the basic processes of risk planning, risk identification, qualitative and quantitative risk analysis, risk response planning and risk monitoring and control.
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